First, we create a dataframe with only numeric columns ( df_num). We want to see students with the lowest grades at the top of the table, so we choose Sort Ascending option from the drop-down menu: In the end, we save the curated dataframe under the port_final name in the student_performance_space. The exploration of correlations is one of the most important steps in EDA. The same is true for the mathematics dataset (we saved it as mat_final table). In this post, we will explore the student performance dataset available on Kaggle. They should be properly rewarded and most important, feel that they have a reasonable chance to win or achieve high mark (Shindler Citation2009). Types of data are accessible via the dtypes attribute of the dataframe: All columns in our dataset are either numerical (integers) or categorical (object). You are not required to obtain permission to reuse this article in part or whole. We have seen the distribution of sex feature in our dataset. Netflix Data: Analysis and Visualization Notebook. Application of deep learning methods for academic performance estimation is shown. Low-Level: interval includes values from 0 to 69. (Citation2015) discussed the participation of students in externally run artificial intelligence competitions. Also, visualization is recommended to present the results of the machine learning work to different stakeholders. Figure 4 (top row) shows performance on the classification and regression questions, respectively, against their frequency of prediction submissions for the three student groups (CSDM classification and regression, ST-PG regression) competitions. Similarly, classification students do better on classification questions (11 vs. 3). Joint learning method with teacher-student knowledge distillation for mrwttldl/Student-Performance-Dataset-Project - Github Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). There are 270 of the parents answered survey and 210 are not, 292 of the parents are satisfied from the school and 188 are not. On the other hand, the predictive accuracy improved with the number of submissions for the regression competitions. A short description of the datasets, including the variables description, is given in the Online Supplementary file. You can even create your own access policy here. In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks. Prediction of student's performance became an urgent desire in most of educational entities and institutes. EDA helps to figure out which features your data has, what is the distribution, is there a need for data cleaning and preprocessing, etc. to 1 hour, or 4 - >1 hour) 14 studytime - weekly study time (numeric: 1 - <2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - >10 hours) 15 failures - number of past class failures (numeric: n if 1<=n<3, else 4) 16 schoolsup - extra educational support (binary: yes or no) 17 famsup - family educational support (binary: yes or no) 18 paid - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no) 19 activities - extra-curricular activities (binary: yes or no) 20 nursery - attended nursery school (binary: yes or no) 21 higher - wants to take higher education (binary: yes or no) 22 internet - Internet access at home (binary: yes or no) 23 romantic - with a romantic relationship (binary: yes or no) 24 famrel - quality of family relationships (numeric: from 1 - very bad to 5 - excellent) 25 freetime - free time after school (numeric: from 1 - very low to 5 - very high) 26 goout - going out with friends (numeric: from 1 - very low to 5 - very high) 27 Dalc - workday alcohol consumption (numeric: from 1 - very low to 5 - very high) 28 Walc - weekend alcohol consumption (numeric: from 1 - very low to 5 - very high) 29 health - current health status (numeric: from 1 - very bad to 5 - very good) 30 absences - number of school absences (numeric: from 0 to 93) # these grades are related with the course subject, Math or Portuguese: 31 G1 - first period grade (numeric: from 0 to 20) 31 G2 - second period grade (numeric: from 0 to 20) 32 G3 - final grade (numeric: from 0 to 20, output target), P. Cortez and A. Silva.

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